A STUDY ON VISCERAL ADIPOSITY INDEX IN PATIENTS WITH ACUTE CORONARY SYNDROME
Dissertation Submitted to
THE TAMIL NADU DR.M.G.R.MEDICAL UNIVERSITY Chennai
In partial fulfilment of the regulations For the award of the degree of
M.D. BRANCH – I (GENERAL MEDICINE)
CHENGALPATTU MEDICAL COLLEGE & HOSPITAL THE TAMIL NADU DR.M.G.R.MEDICAL UNIVERSITY
TAMILNADU, INDIA
APRIL 2017
CERTIFICATE
This is to certify that the dissertation titled “A STUDY ON VISCERAL ADIPOSITY INDEX IN PATIENTS WITH ACUTE CORONARY SYNDROME”, is a bonafide work of Dr. K.SARANYA DEVI in partial fulfilment of the requirements for M.D.BRANCH-I (GENERAL MEDICINE) examination of THE TAMILNADU DR.M.G.R MEDICAL UNIVERSITY to be held in April 2017. The period of study was from January 2016 to June 2016.
Signature of the H.O.D Signature of the DEAN
Prof. Dr. V.R. Mohan Rao, M.D., Prof. Dr. N.Gunasekaran, M.D.,D.C.T.D Professor and Head, Dean,
Department of General Medicine Chengalpattu Medical College, Chengalpattu Medical College, Chengalpattu
Chengalpattu
Date:
Place :
THE TAMILNADU DR.M.G.R MEDICAL UNIVERSITY DECLARATION BY THE CANDIDATE
I, Dr. K. SARANYA DEVI, hereby declare that this dissertation titled “A STUDY ON VISCERAL ADIPOSITY INDEX IN PATIENTS WITH ACUTE CORONARY SYNDROME” is a bonafide and genuine research work done by me at CHENGALPATTU MEDICAL COLLEGE & HOSPITAL from January 2016 – June 2016 under the direct guidance and supervision of DR.M.ANUSUYA, M.D., Professor, Department of General Medicine, Chengalpattu Medical College and Hospital, Chengalpattu. This dissertation is submitted to the Tamil Nadu Dr. M.G.R. Medical University in partial fulfilment of the University regulations for the award of MD degree in General Medicine Examination to be held in April 2017.
Date: Signature of the Candidate
Place: Chengelpattu Dr. K. SARANYA DEVI
SPECIAL ACKNOWLEDGEMENT I gratefully acknowledge and thank Prof. Dr. N.Gunasekaran, M.D.,D.C.T.D
DEAN
CHENGALAPTTU MEDICAL COLLEGE AND HOSPITAL , CHENGALPATTU .
For granting me permission to utilize the resources of this institution for my study
ACKNOWLEDGEMENT
At the outset, it is with a sense of accomplishment and deep gratitude that I dedicate this dissertation to all those who have been instrumental in its completion.
First and foremost I express my heartful thanks to my esteemed and respected HOD Department of General Medicine, Chengalpattu Medical College Dr. V.R. Mohan Rao, M.D., for his supervision and keen interest in academic activities.
I would like to take this opportunity to express my deep sense of gratitude towards my guide Prof. DR. M.ANUSUYA,M.D., Professor , Department of General Medicine , Chengalpattu Medical College , Chengalpattu for her valuable guidance , constant encouragement and expert suggestions which was most crucial in overcoming various difficulties . Had it not been for her whole hearted support throughout the period of this study, extending from her vast knowledge, invaluable advice and constant motivation, I truly would not have been able to complete this dissertation topic in its present form.
I am thankful to Dr. R.Narmadha M.D., Dr. Shaik Sulaiman, M.D., Dr. Ramesh,M.D., Professors of Medicine for their guidance and timely help.
I sincerely thank my Assistant Professors in Department of General Medicine, Dr.Banugopanar, M.D., Dr.Dhurgesananthini, M.D., Dr.N.Ravishankar, M.D., and for giving me practical suggestions and guiding me to carry out this study .
I would sincerely thank Dr. Rahothaman, M.D.,D.M (Cardiology ) for his valuable guidance and support given during study .
I also sincerely thank my Assistant Professors in Department of Social and Preventive Medicine Dr. Parameshwari,M.D., Dr. BanumathyM.D., for helping me with the statistical analysis of the data.
I am deeply indebted to all the teaching staff and my fellow postgraduates for their helpful attitude and valuable suggestions in every stage of my study.
I thank all others who have assisted me in some form or the other in preparation of my thesis. I thank the ethics committee for permitting me to do this study and more importantly I thank all my patients involved for their kind help and co-operation.
I would like to thank my husband and my family for their moral support. Last but not the least; I am grateful to all those patients who were subjects for the study, without whose co-operation this work would not have been possible.
I bow my head in respect before God Almighty.
Date: Signature of the Candidate
Place: Dr. K. SARANYA DEVI
TABLE OF CONTENTS CHAPTER
NO. TITLE PAGE
NO.
I INTRODUCTION 1
II AIM AND OBJECTIVES 4
III REVIEW OF LITERATURE 5
IV CORONARY HEART DISEASE
· Introduction
· CHD in India
· Risk factors for CAD
· Cardinal features of CAD among Indians
V OBESITY
· Obesity and CAD
· Physiologic regulation of Energy Balance
· Adipocyte and Adipose Tissue
· Etiology of Obesity
· Specific Genetic Syndromes
· Metabolic Syndrome
· Pathological consequences of Obesity
11
VI CLINICAL AND LABORATORY ASSESSMENT OF OBESITY
· BMI
· Waist Circumference
· Waist-Hip Ratio
· Skin Fold Measurement
· Bio Electrical Impedence
· DEXA
· MRI
· CT
· NIR
· Underwater weighing
· Visceral Adiposity index
28
CHAPTER
NO. TITLE PAGE
NO.
VII MATERIALS AND METHODS 48
VIII RESULTS AND ANALYSIS 53
IX DISCUSSION 75
X CONCLUSION 81
XI ANNEXURES
BIBLIOGRAPHY PROFORMA MASTER CHART
LIST OF TABLES
TABLE NO
TITLE PAGE
NO 1 Gender distribution of Participants 53
2 Age distribution of Participants 55
3 Diagnosis of Participants 56
4 Prevalence of Diabetes in Participants 57 5 Prevalence of Hypertension in Participants 58
6 Smokers among Participants 59
7 Alcoholics among Participants 60
8 BMI distribution of Participants 61
9 Mean Weight of Participants 62
10 Mean Height of Participants 63
11 Mean BMI of Participants 64
12 BMI with Gender 65
13 Mean WC of Participants 67
14 Mean TG of Participants 68
15 Mean HDL of Participants 69
16 Mean VAI of Participants 70
17 VAI with Age range 71
18 VAI with Gender 73
LIST OF GRAPHS
GRAPH
NO TITLE PAGE
NO 1 Gender distribution of Participants 54
2 Age distribution of Participants 55
3 Diagnosis of Participants 56
4 Prevalence of Diabetes in Participants 57 5 Prevalence of Hypertension in Participants 58
6 Smokers among Participants 59
7 Alcoholics among Participants 60
8 BMI distribution of Participants 61
9 Bar Diagram – Mean Weight of Participants 62 10 Bar Diagram - Mean Height of Participants 63 11 Bar Diagram - Mean BMI of Participants 64
12 BMI with Gender 66
13 Bar Diagram - Mean WC of Participants 67 14 Bar Diagram - Mean TG of Participants 68 15 Bar Diagram - Mean HDL of Participants 69 16 Bar Diagram - Mean VAI of Participants 70
17 VAI with Age range 72
18 VAI with Gender 73
LIST OF ABBREVIATIONS USED
% Percentage
CAD Coronary Artery Disease CHD Coronary Heart Disease
BMI Body Mass Index
WC Waist Circumference
TG Triglycerides
HDL High Density Lipoprotein
VAI Visceral Adiposity Index
WHR Waist Hip Ratio
NAFLD Non Alcoholic Fatty Liver Disease NASH Non Alcoholic Steato Hepatitis PCOD Polycystic Ovarian Disease
WHO World Health Organisation
DM Diabetes
SHT Systemic Hypertension
WT Weight
HT Height
MRI Magnetic Resonance Imaging
CT Computed Tomography
STEMI ST segment elevation myocardial infarction NSTEMI Non ST segment elevation MI.
INTRODUCTION
Coronary heart disease (CHD) is a health epidemic present worldwide. Results of mortality data from Global Burden of Diseases Studies states that CVD especially CAD are important cause of death in India.
Major causes of death around the world: cardiovascular disease, Cancer, Chronic respiratory diseases, HIV/AIDS, TB, Diabetes.
Worldwide 30 % of all deaths are attributed to CVD, out of which more than half are caused by CHD. It is expected that in future the number will grow due to lifestyle changes in developing countries. Of those dying from CVD, 80% are in developing countries.
Myocardial infarction in India:
The incidence of MI in India is steadily increasing over the past few decades. Rural to urban ratio in rise in MI cases is around 2:9. This could probably be due to migration from rural to urban cities. Another major problem is the occurrence of MI at a younger age in the Indian population.
STEMI is an emergency due to acute total occlusion of an epicardial coronary artery, most often due to atherosclerotic plaque rupture / erosion and subsequent thrombus formation. Compared to UA /
NSTEMI, STEMI is associated with a higher in -hospital and 30 day morbidity and mortality. If untreated, the mortality rate of STEMI can exceed 30 % and the presence of mechanical complications increases the mortality rate to 90%.
Obesity is an important risk factor for cardiovascular disease and is an increasing pandemic across the globe. Although BMI has tra- ditionally been the standard index to classify and define obesity, nowadays importance has been given to other indices of adiposity such as waist circumference, waist hip ratio, waist height ratio, atherogenic index, body adiposity index and visceral adiposity index.
· Measurement of the degree of visceral fat has become an important determinant of the degree of cardiovascular risk.
· Visceral adiposity is closely related to CVD via increased adipokine production, proinflammatory activity and the deterioration of insulin sensitivity.
· A study concluded that association between visceral fat and BMI was only minimal and visceral adiposity was able to stratify cardiovascular risk better than BMI.
· The gold standard assessment method for the estimation of visceral fat content has been Magnetic resonance imaging and Computerized tomography.
· These methods are not used due to the cost and time involved in their utilization.
· Amato et al. have recently individualized a novel sex-specific index based on waist circumference, body mass index (BMI), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), and indirectly expressing visceral fat and termed it the visceral adiposity index (VAI).
· VAI had significant correlation with visceral adiposity and its increase was strongly associated with cardiometabolic risk.
· Studies have used the visceral adiposity index using the formula in different populations such as women with PCOS, patients with diabetes and general population without metabolic syndrome.
However there are limited studies that have used visceral adiposity index in patients with myocardial infarction.
Unger and Scherer proposed defining hypoleptinemia as a state of visceral adiposity and ‘‘high TG/low HDL cholesterol dyslipidemia,’’
which were not observed in generalized obesity. Hence, a factor like VAI needs to be evaluated in delineating visceral adiposity and thus more precisely cardiometabolic risk.
AIM AND OBJECTIVES
· To calculate the VAI in patients with acute coronary syndrome.
· To study the factors influencing high VAI among patients with acute coronary syndrome
REVIEW OF LITERATURE
INTRODUCTION
IHD causes more deaths and disability and incurs greater Economic costs than any other illness in the world. Genetic factors, a high-fat and energy-rich diet, smoking and a sedentary lifestyle are associated with the emergence of IHD. IHD is growing among low- income groups, but primary prevention has delayed the disease to later in life in all socioeconomic groups. But epidemiologic data show a decline in the rate of deaths due to IHD, about half of which is attributable to treatments and half to prevention by risk factor modification.
Powerful risk factors for IHD like Obesity, insulin resistance, and type 2 diabetes mellitus are increasing. These trends are occurring due to population growth and as a result of the increase in the average age of the world’s population. With urbanization in countries with emerging economies and a growing middle class, elements of energy rich Western diet are being adopted.
As a result, the prevalence of risk factors for IHD and the prevalence of IHD itself are both increasing rapidly, so that in Analyses of the global burden of disease, there is a shift from communicable to non-communicable diseases. Population subgroups that appear to be particularly affected are men in South Asian countries, especially India
and the Middle East. IHD is likely to become the most common cause of death worldwide by 2020.
Coronary heart disease in India
India is going through an epidemiologic transition whereby the burden of communicable diseases have declined slowly, but that of non- communicable diseases (NCD) has risen rapidly, thus leading to a dual burden. There has been a 4-fold rise of CHD prevalence in India during the past 40 years.Current estimates from epidemiologic studies from various parts of the country indicate a prevalence of CHD to be between 7% and 13% in urban and 2% and 7% in rural populations . Epidemiologic studies have shown that there are at present over 30 million cases of CHD in India. The Global Burden of Diseases Study reported that the disability-adjusted life years lost by CHD in India during 1990 was 5.6 million in men and 4.5 million in women; the projected figures for 2020 were 14.4 million and 7.7 million in men and women respectively. There are no prospective studies in India that have determined factors of risk for CHD. Risk factors for premature CHD have been quantified in the case control INTERHEART study. In the INTERHEART study, 8 common risk factors explained >90% of incident acute myocardial infarctions in South Asian and Indians. Risk factors cited in interheart study;
· Dyslipidemia (high apolipoprotein B/apolipoprotein A1 ratio),
· Smoking or tobacco use,
· Known hypertension,
· Known diabetes,
· Abdominal obesity,
· Physical inactivity,
· Low fruits and vegetables intake and
· Psychosocial stress.
An important change in risk factor dynamics in India is a more rapid increase in CVD risk factors in rural and slum populations compared with urban populations. Smoking and non-smoked tobacco continues to increase in rural and less literate populations, while it is declining in more educated urban populations.
The epidemic of sedentariness has penetrated rural households with rapidly increasing use of labour-saving technologies. Dietary habits have undergone a change with greater consumption of fats, saturated fats, trans fats, and processed foods. Calorie-dense fast foods (comfort foods) are easily available and both Indian-style and Western-style fast foods are being consumed widely.
Increasing abdominal obesity and other cardiometabolic risk factors among the rural populations in India. This Further escalates the CHD epidemic in India.
Other Risk Factors
A number of case-control studies have reported that abnormalities of lipids other than low-density lipoprotein cholesterol may be important in Indians. These lipoprotein lipids include low high-density lipoprotein cholesterol and high triglycerides, very-low-density lipoprotein cholesterol metabolites, lipoprotein remnants, and lipoprotein.
MODIFIABLE AND NONMODIFIABLE RISK FACTORS FOR CAD
· High LDL cholesterol
· Cigarette smoking
· Hypertension (BP ≥140/90 mmHg or on antihypertensive medication)
· Low HDL cholesterol (<1.0 mmol/L <40 mg/dL)
· Diabetes mellitus
· Family history of premature CHD
· Age (men ≥45 years; women ≥55 years)
Lifestyle risk factors
· Obesity (BMI ≥30 kg/m2)
· Physical inactivity
Emerging risk factors
· Lipoprotein (a)
· Prothrombotic factors
· Proinflammatory factors
· Impaired fasting glucose
· Subclinical atherosclerosis
(HDL cholesterol ≥1.6 mmol/L (≥60 mg/dL) has been viewed as a
“negative” risk factor).
CARDINAL FEATURES OF CORONARY ARTERY DISEASE AMONG INDIANS COMPARED TO OTHER POPULATIONS
• Higher rates 2 to 4 fold higher prevalence, incidence, hospitalization and mortality.
• Greater prematurity
- 5 to 10 year earlier onset of first Myocardial infarction (Ml) - 5 to 10 fold higher rate of Ml and death in young «40 years
of age)
• Greater severity
- Three vessel disease common even among young premenopausal Women.
- Large Ml with greater muscle damage
• Higher prevalence of glucose intolerance- Insulin resistance syndrome, diabetes, central obesity.
• Lower prevalence of conventional risk factors - Hypertension, cigarette smoking
• Higher prevalence of emerging (thrombogenic) risk factors - High levels of lipoprotein a, homocysteine, Apo protein B - High levels of triglycerides, fibrinogen
- Low levels of HDL - Small dense LDL
• Higher rates of clinical events for a given degree of atherosclerosis - Double that of Whites
- 4 fold higher than Chinese
- Higher proportion of unstable or vulnerable plaques.
OBESITY AND CORONARY ARTERY DISEASE
Obesity is a state of excess adipose tissue mass. Although often viewed as equivalent to increased body weight, this need not be the case, muscular individuals may be overweight by numerical standards without having increased adiposity. Body weights are distributed continuously in populations, so that choice of a medically meaningful distinction between lean and obese is somewhat arbitrary. Obesity is therefore defined by assessing its linkage to morbidity or mortality.
Although not a direct measure of adiposity, the most widely used method to gauge obesity is the body mass index (BMI), which is equal to weight/height2 (in kg/m2). Other approaches to quantifying obesity include;
· visceral adiposity index
· waist hip ratio
· waist circumference
· anthropometry (skinfold thickness),
· densitometry (underwater weighing),
· computed tomography (CT) or
· magnetic resonance imaging (MRI), and
· Electrical impedance.
The distribution of adipose tissue in different anatomic depots also has substantial implications. Specifically, intraabdominal and abdomen subcutaneous fat have more significance than subcutaneous fat present in the buttocks and lower extremities. This distinction is most easily made clinically by determining the waist-to -hip ratio, with a ratio >0.9 in women and >1.0 in men being abnormal.
The mechanism underlying this association is unknown but may relate to the fact that intraabdominal adipocytes are more lipolytically active than those from other depots. Release of free fatty acids into the portal circulation has adverse metabolic actions, especially on the liver.
Adipokines and cytokines that are differentially secreted by adipocyte depots may play a role in the systemic complications of obesity.
PHYSIOLOGIC REGULATION OF ENERGY BALANCE
Body weight is regulated by both endocrine and neural components that ultimately influence the effector arms of energy intake
and expenditure. This complex regulatory system is necessary because even small imbalances between energy intake & expenditure will ultimately have large effects on body weight. This regulation of energy balance cannot be monitored easily by calorie-counting in relation to physical activity. Rather, regulation or dysregulation depends on a complex interplay of hormonal and neural signals.
Alterations in stable weight by forced overfeeding or food deprivation induce physiologic changes that resist these perturbations:
· With weight loss, appetite increases and energy expenditure falls;
· With over feeding, appetite falls and energy expenditure increases.
This latter compensatory mechanism frequently fails, however, permitting obesity to develop when food is abundant and physical activity is limited.
A major regulator of these adaptive responses is the adipocyte- derived hormone leptin, which acts through brain circuits predominantly in the hypothalamus to influence appetite, energy expenditure, and neuroendocrine function. Appetite is influenced by many factors that are integrated by the brain, most importantly within the hypothalamus.
Signals that impinge on the hypothalamic centre include;
1. Neural afferents, 2. Hormones, and 3. Metabolites.
Neural – vagal inputs are particularly important, bringing information from viscera, such as gut distention.
Hormonal signals - leptin, insulin, cortisol, and gut peptides.
Among the latter is ghrelin, which is made in the stomach and stimulates feeding, peptide YY (PYY) and cholecystokinin, which is made in the small intestine and signals to the brain through direct action on hypothalamic control centres and or via the vagus.
Metabolites – glucose can influence appetite, as seen by the effect of hypoglycemia to induce hunger; however, glucose is not normally a major regulator of appetite.
These diverse signals act by influencing the expression and release of various hypothalamic peptides (e.g., neuropeptide Y NPY, Agoutirelated peptide AgRP, α-melanocyte-stimulating hormone α MSH, and melanin-concentrating hormone MCH) that are integrated with serotonergic, catecholaminergic, endocannabinoid, and opioid signalling pathways.
Energy expenditure includes the following components:
· resting or basal metabolic rate;
· the energy cost of metabolizing and storing food;
· the thermic effect of exercise.
Adaptive thermogenesis occurs in brown adipose tissue (BAT). In contrast to white adipose tissue, which is used to store energy in the form of lipids, BAT expends stored energy as heat. A mitochondrial uncoupling protein (UCP-1) in BAT dissipates the hydrogen ion gradient in the oxidative respiration chain and releases energy as heat. The metabolic activity of BAT is increased by a central action of leptin, acting via sympathetic nervous system that heavily innervates this tissue.
In rodents, BAT deficiency causes obesity and diabetes; stimulation of BAT with a specific adrenergic agonist (β3 agonist) protects against diabetes and obesity. BAT exists in humans (especially neonates), and although its physiologic role is not yet established, identification of functional BAT in many adults using positron emission tomography(PET) imaging has increased interest in the implications of the tissue for pathogenesis and therapy of obesity. Beige fat cells, recently described, resemble BAT cells in expressing UCP-1. They are scattered through white adipose tissue, and their thermogenic potential is uncertain.
t THE ADIPOCYTE AND ADIPOSE TISSUE
Adipose tissue is composed of the lipid-storing adipose cell &
stromal/vascular compartment in which cells including preadipocytes and macrophages reside. Adipose mass increases by enlargement of adipose cells through lipid deposition, as well as by an increase in the number of adipocytes.
Obese adipose tissue is also characterized by increased numbers of infiltrating macrophages. The process by which adipose cells are derived from a mesenchymal preadipocyte involves an series of differentiation steps mediated specific transcription factors. One of the key transcription factors is peroxisome proliferator -activated receptor γ (PPARγ), a
nuclear receptor that binds the thiazolidinedione class of insulin- sensitizing drugs used in the treatment of type 2 diabetes.
It is also an endocrine cell that releases leptin, cytokines such as tumor necrosis factor(TNF)-α and interleukin (IL)-6, complement factors such as factorD (also known as adipsin), prothrombotic agents such as plasminogen activator inhibitor I, and a component of the blood pressure–regulating system, angiotensinogen.
Adiponectin, an abundant adipose- derived protein whose levels are reduced in obesity, enhances insulin sensitivity and lipid oxidation and has vascular-protective effects, whereas resistin and RBP4, whose levels are increased in obesity, may induce insulin resistance. These factors, and others not yet identified, play a role in the physiology of lipid homeostasis, insulin sensitivity, blood pressure control, coagulation, and vascular health, and are likely to contribute to obesity- related pathologies.
ETIOLOGY OF OBESITY
Molecular pathways regulating energy balance are the causes of obesity remain elusive. This reflects the fact that obesity is a heterogeneous group of disorders. At one level, the pathophysiology of obesity seems simple: a chronic excess of nutrient intake relative to the level of energy expenditure. However, due to the complexity of the
neuroendocrine and metabolic systems that regulate energy intake, storage, and expenditure, it has been difficult to quantitate all the relevant parameters (e.g., food intake and energy expenditure) over time in human subjects.
Specific Genetic Syndromes
Distinct mutations cause both hyperphagia and diminished energy expenditure, suggesting a physiologic link between these two parameters of energy homeostasis.
Identification of the ob gene mutation in genetically obese (ob/ob) mice represented a major breakthrough in the field. The ob/ob mouse develops severe obesity, insulin resistance, and hyperphagia, as well as efficient metabolism (e.g., it gets fat even when ingesting the same number of calories as lean litter mates). The product of the ob gene is the peptide leptin, a name derived from the Greek root leptos, meaning thin.
Leptin is secreted by adipose cells and acts primarily through the hypothalamus. Its level of production provides an index of adipose energy stores. High leptin levels decrease food intake and increase energy expenditure. Another mouse mutant, db/db, which is resistant to leptin, has a mutation in the leptin receptor and develops a similar syndrome. The ob gene is present in humans where it is also expressed in fat.
· Mutations in the gene encoding proopiomelanocortin (POMC) cause severe obesity through failure to synthesize α-MSH, a key neuropeptide that inhibits appetite in the hypothalamus.
· Proenzyme convertase 1 (PC-1) mutations cause obesity by preventing synthesis of α-MSH from its precursor peptide, POMC.
· α-MSH bind s to the type 4 melanocortin receptor (MC4R), a key hypothalamicreceptor that inhibits eating. Heterozygous loss-of-function mutations of this receptor cause obesity.
· Loss of function of MRAP2, a protein required for normal MC4R signaling, has been found in rare cases of severe obesity.
· These six genetic defects define a pathway through which leptin (by stimulating POMC and increasing α-MSH) restricts food intake and limits weight.
The fat gene encodes carboxypeptidase E, a peptide-processing enzyme; mutation of this gene is thought to cause obesity by disrupting production of one or more neuropeptides. AgRP is coexpressed with NPY in arcuate nucleus neurons. AgRP antagonizes α-MSH action at MC4 receptors, and its overexpression induces obesity.
SYNDROMES ASSOCIATED WITH OBESITY(CONGENITAL)
· Prader-Willi syndrome
· Bardet-Biedl syndrome(BBS)
METABOLIC SYNDROME
The metabolic syndrome (syndrome X, insulin resistance syndrome) consists of a constellation of metabolic abnormalities that confer increased risk of cardiovascular disease (CVD) and diabetes mellitus. Evolution of the criteria for the metabolic syndrome since the original definition by the World Health Organization in 1998 reflects growing clinical evidence and analysis by a variety of consensus conferences and professional organizations. The major features of the metabolic syndrome include central obesity, hypertriglyceridemia, low levels of high-density lipoprotein (HDL) cholesterol, hyperglycemia, and hypertension.
NCEP: ATPIII (a) 2001 and Harmonizing Definition Criteria for the Metabolic Syndrome.
NCEP: ATPIII 2001
Three or more of the following:
• Central obesity: waist circumference >102 cm (M), >88 cm(F)
• Hypertriglyceridemia: triglyceride level ≥150 mg/dL or specific medication
• Low HDL cholesterol: <40 mg/dL and <50 mg/dL for men and women, respectively, or specific medication
• Hypertension: blood pressure ≥130 mmHg systolic or ≥85 mmHg diastolic or specific medication.
• Fasting plasma glucose level ≥100 mg/dL or specific medication or previously diagnosed type 2 diabetes.
Harmonizing Definition Three of the following:
• Waist circumference (cm) Men Women Ethnicity
≥94 ≥80 Europid, sub-Saharan African, Eastern.
≥90 ≥80 South Asian, Chinese, South and Central America.
≥85 ≥90 Japanese
• Fasting triglyceride level >150 mg/dL or specific medication.
• HDL cholesterol level <40 mg/dL and <50 mg/dL for men and women, respectively, or specific medication.
• Blood pressure >130 mm systolic or >85 mm diastolic or previous diagnosis or specific medication.
• Fasting plasma glucose level ≥100 mg/dL (alternative indication:
drug treatment of elevated glucose levels).
• a - National Cholesterol Education Program and Adult Treatment Panel III.
PATHOLOGIC CONSEQUENCES OF OBESITY
Obesity has major adverse effects on health. Obesity is associated with an increase in mortality , with a 50–100% increased risk of death from all causes compared to normal -weight individuals , mostly due to cardiovascular causes.
Obesity and overweight together are the second leading cause of preventable death in the United States, accounting for 300,000 deaths per year. Mortality rates rise as obesity increases, particularly when obesity is associated with increased intraabdominal fat.
Life expectancy of a moderately obese individual could be shortened by 2–5 years, and a 20- to 30-year-old male with a BMI >45 may lose 13 years of life. It is likely that the degree to which obesity affects particular organ systems is influenced by susceptibility genes that vary in the population. Insulin Resistance and Type 2 Diabetes Mellitus Hyperinsulinemia and insulin resistance are pervasive features of
obesity, increasing with weight gain and diminishing with weight loss.
Insulin resistance is more strongly linked to intraabdominal fat than to fat in other depots .
Molecular links between obesity and insulin resistance in fat, muscle, and liver have been sought for many years. Major factors include:
· insulin itself, by inducing receptor downregulation;
· free fatty acids that are increased and capable of impairing insulin action;
· intracellular lipid accumulation; and
· Several circulating peptides produced by adipocytes, including the cytokines TNF-α and IL-6, RBP4, and the “adipokines”
adiponectin and resistin, which have altered expression in obese adipocytes and can modify insulin action.
· Additional mechanisms are obesity-linked inflammation, includinginfiltration of macrophages into tissues including fat, and induction of the endoplasmic reticulum stress response, which can bring about resistance to insulin action in cells.
· Despite the prevalence of insulin resistance, most obese individuals do not develop diabetes, suggesting that diabetes
requires an interaction between obesity-induced insulin resistance and other factor s such as impaired insulin secretion. Obesity, however, is a major risk factor for diabetes, and as many as 80%
of patients with type 2 diabetes mellitus are obese.Weight loss and exercise , even of modest degree, increase insulin sensitivity and often improve glucose control in diabetes.
· Reproductive Disorders Disorders that affect the reproductive axis are associated with obesity in both men and women. Male hypogonadism is associated with increased adipose tissue, often distributed in pattern more typical of females. In men whose weight is >160% ideal body weight (IBW), plasma testosterone and sex hormone–binding globulin (SHBG) are often reduced, and estrogen levels (derived from conversion of adrenal androgens in 3 adipose tissue) are increased. Gynecomastia may be seen.
However, masculinization, libido, potency, and spermatogenesis are preserved in most of these individuals. Free testosterone may be decreased in morbidly obese men whose weight is >200%
IBW.
Obesity has long been associated with menstrual abnormalities in women, particularly in women with upper body obesity. Common findings are increased androgen production, decreased SHBG, and increased peripheral conversion of androgen to estrogen. Most obese
women with oligomenorrhea have polycystic ovarian syndrome (PCOS), with its associated anovulation and ovarian hyperandrogenism; 40% of women with PCOS are obese. Most nonobese women with PCOS are also insulin-resistant, suggesting that insulin resistance, hyperinsulinemia, or the combination of the two are causative or contribute to the ovarian pathophysiology in PCOS in both obese and lean individuals. Increasing evidence supports a role for adipokines in mediating a link between obesity and the reproductive dysfunction of PCOS. In obese women with PCOS, weight loss or treatment with insulin-sensitizing drugs often restores normal menses. The increased conversion of androstenedione to estrogen, which occurs to a greater degree in women with lower body obesity, may contribute to the creased incidence of uterine cancer in postmenopausal women with obesity.
Cardiovascular Disease the Framingham Study revealed that obesity was an independent risk factor for the 26-year incidence of cardiovascular disease in men and women (including coronary disease, stroke and congestive heart failure). The waist-to-hip ratio may be the best predictor of these risks. When the additional effects of hypertension and glucose intolerance associated with obesity are included, the adverse impact of obesity is even more evident. The effect of obesity on cardiovascular mortality in women may be seen at BMIs as low as 25.
Obesity, especially abdominal obesity, is associated with an atherogenic lipid profile; with increased low-density lipoprotein cholesterol, very-
low-density lipoprotein, and triglyceride; and with decreased high- density lipoprotein cholesterol and decreased levels of the vascular protective adipokine adiponectin.
Obesity is also associated with hypertension. Measurement of blood pressure in the obese requires use of a larger cuff size to avoid artefactual increases. Obesity-induced hypertension is associated with increased peripheral resistance and cardiac output, increased sympathetic nervous system tone, increased salt sensitivity, and insulin-mediated salt retention; it is often responsive to modest weight loss.
Pulmonary Disease Obesity may be associated with a number of pulmonary abnormalities. These include reduced chest wall compliance, increased work of breathing, increased minute ventilation due to increased metabolic rate, and decreased functional residual capacity and expiratory reserve volume. Severe obesity may be associated with obstructive sleep apnea and the “obesity hypoventilation syndrome” with attenuated hypoxic and hypercapnic ventilatory responses. Sleep apnea can be obstructive (most common), central, or mixed and is associated with hypertension.
Weight loss (10–20 kg) can bring substantial improvement, as can major weight loss following gastric bypass or restrictive surgery.
Continuous positive airway pressure has been used with some success.
Hepatobiliary Disease Obesity is frequently associated with non- alcoholic fatty liver disease (NAFLD), and this association represents one of the most common causes of liver disease in industrialized countries. The hepatic fatty infiltration of NAFLD progresses in a subset to inflammatory nonalcoholic steatohepatitis (NASH) and more rarely to cirrhosis and hepatocellular carcinoma. Steatosis typically improves following weight loss, secondary to diet or bariatric surgery. The mechanism for the association remains unclear.
Obesity is associated with enhanced biliary secretion of cholesterol, supersaturation of bile, and a higher incidence of gallstones, particularly cholesterol gallstones. A person 50% above IBW has about a six fold increased incidence of symptomatic gallstones. Paradoxically, fasting increases supersaturation of bile by decreasing the hospholipid component.Fasting- induced cholecystitis is a complication of extreme diets.
Cancer Obesity is associated with increased risk of several cancer Types and in addition can lead to poorer treatment outcomes and increased cancer mortality. Obesity in males is associated with higher mortality from cancer of the esophagus, colon, rectum, pancreas, liver,and prostate; obesity in female s is associated with higher mortality from cancer of the gallbladder, bile ducts, breasts, endometrium, cervix and ovaries. Some of the latter may be due to increased rates of
conversion of androstenedione to estrone in adipose tissue of obese individuals. Other possible mechanistic links may involve hormones, growth factors, and cytokines whose levels are linked to nutritional state, including insulin, leptin, adiponectin, and IGF-I, as well as activation of signaling pathways linked to both obesity and cancer.
Bone, Joint, and Cutaneous Disease Obesity is associated with an increased risk of osteoarthritis, no doubt partly due to the trauma of added weight bearing, but potentially linked as well to activation of inflammatory pathways that could promote synovial pathology. The prevalence of gout may also be increased.
One of the skin problems associated with obesity is acanthosis nigricans, manifested by darkening and thickening of the skinfolds on the neck, elbows and dorsal interphalangeal spaces. Acanthosis reflects the severity of underlying insulin resistance and diminishes with weight loss.Friability of skin may be ncreased, especially in skinfolds, enhancing the risk of fungal and yeast infections. Finally, venous stasis is increased in the obese.
CLINICAL AND LABORATORY ASSESSMENT OF OBESE
Methods to assess body composition are numerous and also difficult to organize systematically. The most accurate methods in assessing body fat mass include underwater body density measurement,
body fat content estimation by dual energy X-ray absorptiometer (DEXA), magnetic resonance imaging (MRI), and computerized tomography (CT). These methods are time-consuming and require expensive equipment and thus are not feasible for large epidemiological studies. In epidemiological studies overweight, overall obesity, and abdominal obesity are typically measured by using ratios of body weight and height or body circumferences, such as Body Mass Index (BMI), waist circumference, and Waist Hip Ratio (WHR). In epidemiological studies overweight, overall obesity, and abdominal obesity are typically measured by using ratios of body weight and height or body circumferences, such as BMI, waist circumference, and WHR. BMI, WHR, and waist circumference are continuous variables and when used to define overweight or obesity, their cut-off points are arbitrary.
BODY MASS INDEX (BMI)
The BMI of a person is the number obtained by dividing his weight (in Kilograms) by the square of his height (in meters):
(Weight in Kilograms) BMI = --- (Height in Meters)2
BMI is highly correlated with body weight and is a surrogate measure of total body fat content but is also affected by muscle
mass.World Health Organization (WHO, 2000, p. 8-9) and by the Expert Panel on theIdentification, Evaluation, and Treatment of Overweight and Obesity in Adults (1998) are based on an increased risk of morbidity and mortality in different populations.
BMI <18.5 kg/m2 is defined as underweight 18.5-24.9 kg/m2 as normal weight
25.0-29.9 kg/m2 as overweight
And > 30 kg/m2 is considered as obesity.
Obesity can be further stratified into
o Moderate obesity (BMI 30-34.9 kg/m2) o Severe obesity (35-39.9 kgm2)
o Very severe obesity (≥40 kg/m2)
In adult Asians, a person is considered underweight if his BMI is
<18.5 kg/m²; he is considered normal if his BMI is 18.5 to 22.9 kg/m²;
he is considered overweight if his BMI is > 23 kg/m²; he is considered obese if his BMI is > 25 kg/m².
WAIST CIRCUMFERENCE (WC) *
Adipose tissue (fat) distributed centrally within the abdomen and among the viscera is also a predictor of cardiovascular, Cerebrovascular, and metabolic (diabetes & hyperlipidemia) diseases. The cut-off points of waist circumference and WHR are sex and population-specific. The World Health Organization has recommended the use of a cutoff point for waist circumference of 88 cm in women and 102 cm in menand for WHR of 0.85 in women and 1.0 in men to define an increased health risk (World Health Organization 2000, p. 9-11). The same cut-off points for waist circumference have been recommended by the Expert Panel on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults (1998) and the National Cholesterol Education Program (Ford et al. 2002).
Waist circumference appears particularly useful in the clinical setting, where both BMI and waist girth can be easily measured and followed in time (Despres et al. 2001). Waist circumference, however, is to some extent correlated with body height, and thus tall persons may falsely be categorized into the abdominally obese group. In Asian Indians Cut off values for Waist circumference were 85 and 80 cm for men and women.
Measure the waist circumference as recommended by the WorldHealth Organization:
1. The subject should be lightly dressed; measurement should not be made through thick or bulky clothing.
2. Position the subject upright with feet 25 – 30 cm apart and weight evenly distributed.
3. Sit yourself by the subject comfortable on a chair.
4. Fit a tape measure snugly around the abdominal girth without compressing soft tissue and measure the waist circumference to the nearest 0.1 cm in a horizontal plane midway between the inferior costal margin and the iliac crest.
WAIST-HIP RATIO (WHR)
The Waist-Hip Ratio of a person is obtained by dividing his
“Waist circumference” by his “Hip circumference”:
(Waist Circumference in cm) WHR = --- (Hip circumference in cm)
Hip circumference can be measured around the pelvis at the point of maximal protrusion of the buttocks.
A WHR > 1.0 in European men or 0.85 in European women is associated with increased risk of ischemic heart disease, stroke, and diabetes. In Asian Indians Cutoff values for WHRs were 0.88 and 0.81,respectively for males and females respectively ((Journal of Association of Physicians of India 2007, vol. 55).
WHR is considered to be a ratio between fat stored centrally Inside the abdomen (waist circumference) and fat stored peripherally (hip circumference). Waist girth and WHR are better measures of Intraabdominal fat and probably also of total fat than BMI when Validated against computer tomography or magnetic resonance imaging.
Although the current recommendation seems to favor the use of waist circumference in assessing abdominal obesity, WHR remains Suitable method for research purposes (World Health Organization \ 2000,p.10).
WHR has been criticized due to its inability to classify obesity In follow-up studies, particularly in women, if the subjects gain weight in the waist and hip areas simultaneously (Despres et al. 2001).
Furthermore, it is difficult to interpret whether a large WHR is attributable to a large waist girth or to narrow hips. Previous studies suggest that both narrow waist and large hips may protect against CVD and for this reason, it is recommended that waist and hip girths should be measured (Lissner et al. 2001; Seidell et al. 2001b).
Both BMI and WHR are confounded by sex, age, and ethnic background. Also the distribution of overweight and obesity is different across populations (World Health Organization 2000).In general, men tend to have higher amounts of visceral adipose tissue than women, particularly pre- menopausal women (Lemieux et al.1993), older persons have larger waist circumferences than younger ones ( Molarius and Seidell 1998), and morbidity risks at the same level of overweight vary across different ethnic populations (Seidell et al.2001a). Therefore, instead of globally accepted cut-off points for obesity, there need to be age, sex, and race-specific categories for overweight and obesity.
It is important to understand that BMI, waist circumference, and WHR estimate fat mass at different locations, reflect different etiological perspectives, and thus do not assess identical phenomena.
SKIN FOLD MEASUREMENT
An increase in weight in relation to height does not always Mean fatness or obesity if the increase in weight is due to muscle mass as in body- builders. Degree of fatness (adiposity) correlates better with the increased risks of diseases. Skin fold thickness is often used to measure this adiposity.
Measurement of skin fold thickness requires special calipers.
Various areas of the body have been suggested as suitable for measuring
skin fold thickness, including back of the arm over the triceps, below the scapulae at the back, and at the supra-iliac region of the anterior abdominal wall.
Advantages
• Easy to use once skill has been mastered
• Does not require much time
• Non-invasive method Disadvantages
• Technical sources of error
• Mostly concerned with subcutaneous fat (under the skin)
• May not be an ideal measurement for those who are obese and very lean
BIOELECTRICAL IMPEDANCE
Bioelectrical impedance measures the body’s electrical impedance (resistance) to a small electric current. This impedance measurement can be analyzed to provide an estimate of the adiposity (amount of fat tissue in the body) of a person.
It is recommended the following guidelines be followed :
• Abstain from eating and drinking within 4 hours of the test
• Avoid exercising within 12 hours of the test
• Void (urinate) completely prior to testing
• Do not drink alcohol within 48 hours of the test
• Avoid taking diuretics prior to testing
However, accuracy is dependant upon several client-based variables. Wrong interpretation of adiposity through measurement of bioelectrical impedance can result due to the presence of fluid in the limbs (edema), fluid in the abdominal cavity (ascites), a full urinary bladder, or excessive sweating.
Advantages
• Requires little or no technical knowledge of the operator or the client
• Testing itself takes less than a minute
• The unit can be easily transported from place to place
• Requires only an electrical outlet and the machine itself
Disadvantages
· This method has a higher standard error range than most people desire.
· Tends to consistently overestimate lean people and underestimate obese people.
· The accuracy BIA does have is very dependant on multiple variables which may be hard to control for some people. There is doubt whether bioelectrical impedance measurement offers any advantage over BMI, WC, or WHR measurement.
· Its need of specialized equipment is another disadvantage.
DUAL ENERGY X-RAY ABSORPTIOMETRY (DEXA)
It is based on the three component model of body composition.
DEXA uses two X-ray energies to measure body fat, muscle, and bone mineral. When having the scan done, one must lay still in the supine position on what looks like an x-ray table. It takes approximately twelve minutes for the computer software to reduce an image of the tissues.
The results may be viewed as whole body estimates of body fat, muscle, and bone mineral as well as regional body estimates.
Advantages
• Radiation exposure is low
• DEXA is quick
• One does not have to wear a bathing suit or skimpy clothing
• There is no special preparation on the part of the participant
Disadvantages
· DEXA is costly.
· DEXA takes bone mineral content into consideration when estimating body fat and muscle; therefore it is considered to be more accurate and valid than a two component model of body composition such as underwater weighing.
MAGNETIC RESONANCE IMAGING:-
Cross-sectional abdominal images obtained with MRI, allow for separation of the subcutaneous adipose tissue (SAT) from the visceral adipose tissue (VAT) compartment. Several studies have examined the relationship between the VAT and SAT compartments for adults and children. Equations have been developed for the calculation of the VAT or SAT area based on anthropometric measurements, e.g., waist circumference or skinfolds. Although these equations may predict the
average VAT for a population, they have limited accuracy for the individual. A significant advantage of MRI for body composition studies is its potential for monitoring changes in the VAT and SAT compartments separately, information which is presently not available with any alternate in vivo techniques except for CT. The MRI-derived estimates for abdominal SAT and VAT will serve as a reference measure of adiposity for testing the efficacy of future therapies for obesity.
COMPUTED TOMOGRAPHY
This basic anatomical image is similar to that obtained using MRI, except it contains additional information for the tissue's true density at each pixel. This information coupled with the anatomical location of the pixel within the image can be used to identify it as adipose, muscle, skin, viscera, or bone tissue. Reconstruction of total body mass and separate organ masses based on scans along the length of the body at 10-cm intervals has been shown to have excellent accuracy (<1% error) and precision (<1%). These reconstructed CT images, and those for MRI, can be assigned to level 4 or tissue systems level of the multicompartment model. The CT images can also be used to separate the total adipose tissue mass into its subcutaneous and visceral components, or the lean tissues into skeletal muscle and visceral or organ mass.
NEAR INFRARED INTERACTANCE (NIR)
This method of assessing body fat is based on the principles of light absorption, reflectance, and near infrared spectroscopy. To estimate body composition, a computerized spectrophotometer that has a scan and probe are used. The probe is placed onto a selected body site such as the biceps; it emits an infrared light which passes through both fat and muscle and is reflected back to the probe.
Subject data such as height, weight, sex, age, frame size and activity level are taken into consideration. Density measurements are obtained and incorporated into the manufacturer’s prediction equations.
A digital read out including percentage body fat and lean tissue are displayed.
This method of assessing body fat is not the most accurate. In a study by Mclean et al (1992), it was found that skinfolds more accurately predicted body fat than NIR when underwater weighing was used as the criterion measure. In their study, NIR underestimated body fat by more than 4% in subjects greater than 30% fat and overestimated body fat by 4% in subjects less than 8% fat.
Advantages
• Safe
• Non-invasive
• Fast
• Convenient.
Disadvantages
• Not one of the most accurate techniques used to assess body fat composition.
UNDERWATER WEIGHING (HYDROSTATIC WEIGHING) This method uses Archimedes principle which states that when a body is submerged in water, there is a buoyant counter force equal to the weight of the water which is displaced because bone and muscle are denser than water, a person with a larger percentage of fat free mass will weigh more in the water and have a lower percent body fat. Conversely, fat floats. Therefore, a large amount of fat mass will make the body lighter in the water and have a higher percent body fat. If each test is performed correctly according to the recommended guidelines, there is a +/- 1.5% error.
Advantages
• This method is currently considered the "gold standard" in percent body fat measurement.
• Repeat measures usually prove consistent, and can be used to chart progress.
Disadvantages:
• This method usually requires a lot of equipment and space.
• Testing is time consuming and involved.
• Requires in-depth knowledge to administer the tests and compute the calculations.
• Being submerged under water may be difficult and produce anxiety for some.
VISCERAL ADIPOSITY INDEX
The BMI is wrongly considered a satisfactory predictor of the percentage of body fat, and it is known that it shows a curvilinear and not a linear association with the body fat percentage in both men and women. Beyond the criticisms recently leveled towards the normalization of weight by the square of the height many factors affect the relationship between BMI and body fat percentage, such as gender, race, high muscle mass(e.g., subjects who practice body building), and changes in hydration status (in particular subjects having retention of extracellular fluids may lead to significant mistakes in interpretation about BMI). In older people, significant changes also occur in both BMI
numerator and denominator. Also, in 2005 in JAMA Katherine Flegal published one of the first studies to analyze in detail the correlation between BMI and all causes of mortality on a large series.This study, based on National Health and Nutrition Examination Survey (NHANES) data, showed that BMI is not a good predictor of mortality risk. A subsequent meta-analysis published in Lancet in 2006, evaluating 40 epidemiological studies for a mean follow up of 3.8 years, confirmed Flegal’s data. These data led in 2006 to an editorial published in Lancet, provocatively titled “Should we continue to use BMI as a cardiovascular risk factor?” which states that “the BMI may now be withdrawn permanently as a clinical or epidemiological tool for evaluation of cardiovascular risk in both primary and secondary prevention.”
In fact the BMI does not appear in any of the versions of the algorithm of Framingham, one of the strongest tools for defining coronary and cardiovascular risk. Indeed, in the Framingham study it was assumed that there could be “metabolically healthy obese subjects.”
An important contribution to evaluation of the influence of obesity on cardiovascular risk is the Interheart Study, which shows inconvertible evidence that abdominal obesity makes a higher contribution than BMI to the probability of these events. The following year, focusing attention on the relation between obesity and heart attack risk, Yusuf et al.published a study defining the results more accurately, showing that
the association between abdominal adiposity and coronary heart disease risk is highly significant in all geographical areas in which Interheart Study data were collected.
These data incontrovertibly seem to support the usefulness of the evaluation of waist circumference (WC) in cardiovascular risk stratification, having been considered the most valid index of regional distribution of adipose tissue. Measurement of body circumferences, although it is a valid method, requires an accurate performance method in order to provide reproducible information. Several studies have shown that WC is strongly related to visceral fat and abdominal adiposity, more than BMI and waist/hip ratio. The only limitation of WC is inaccurate distinction between visceral and subcutaneous adipose tissue in the abdominal region.
2. A New Method for Evaluation of Adipose Distribution and Function (Visceral Adiposity Index)
The Visceral Adiposity Index (VAI) is an empirical mathematical model, gender-specific, based on simple anthropometric (BMI and WC) and functional parameters (triglycerides (TG) and HDL cholesterol (HDL)), and indicative of fat distribution and function. It is an empirical- mathematical model that does not originate from theoretical assumptions, but from observation in a healthy normal/overweight population of a linear relationship between BMI and CV.
At first a model of adipose distribution (MOAD) was created based on this linear equation (which shows a strong correlation with visceral fat mass determined by MRI. Subsequently MOAD was corrected for triglyceride and HDL cholesterol levels, determining the VAI:
Females:
VAI = (WC) / 36.58 + (1.89*BMI) * (TG/0.81) * (1.52/HDL) Males:
VAI = (WC) / 39.68 + (1.88*BMI) * (TG/1.03) * (1.31/HDL) where WC is expressed in cm, BMI in Kg/m2, TG in mg/dL, and HDL in mg/dL. Assuming VAI = 1 in healthy non obese subjects with normal adipose distribution and normal TG and HDL levels.
The VAI has shown a strong positive correlation with peripheral glucose utilization during euglycemic hyperinsulinemic clamp and seems to be independently associated with cardio- and cerebrovascular events.
In the last three years, it has been reported on more than 30 publications, in which the capability of the VAI to express a possible “adipose tissue dysfunction and the cardiometabolic risk associated to it have been evaluated.
3. VAI in the General Population as a Marker of Cardiometabolic Risk
The main aim of our research field on the VAI has been to identify a simple clinical marker of adipose tissue dysfunction (indirectly reflecting cardiometabolic risk), before it develops into an overt metabolic syndrome and/or a cardiovascular complication.
Unfortunately, today there are still no longterm prospective studies that allow us to evaluate the predictive power of the VAI regarding cardiovascular risk.
Our first study on the VAI , in a population of 1,498 Caucasian primary care patients, already showed that there was a strong independent association with both cardiovascular (odd ratio (95% CI):
2.45 (1.52–3.95)) and cerebrovascular events (odd ratio (95% CI): 1.63 (1.06–2.50)); in the same study, a receiver operating characteristic (ROC) analysis proved greater sensitivity and specificity of VAI, compared to its individual components (WC, BMI, HDL, and TG) with regard to cardiovascular and cerebrovascular events.
In another ROC analysis on 1518 Peruvian adults, in which various measures of adiposity were evaluated, VAI, WC, and waist- height ratio (WHtR) were the best predictors of the individual components of the metabolic syndrome . In particular, the VAI showed good predictive power regarding the visceral adiposity-related risk of
type 2 diabetes and hypertension . Although in some studies the increase in VAI was significantly associated with a significant reduction in insulin sensitivity ,VAI is not to be seen as an index of insulin sensitivity, but as an indicator of altered adipose function which is associated with insulin resistance.
Few studies connected in different population did not show any superiority of VAI. Hence these data suggest the need for further prospective studies which take into account the greater variability in the years of the VAI, compared to other simple anthropometric measures.
MATERIALS AND METHODS
PLACE OF STUDY:
Chengalpattu Medical College & Hospital, Chengalpattu DURATION OF STUDY:
6 months (Jan 2016 – June 2016) STUDY DESIGN:
Case control cross sectional study.
POPULATION STIDIED:
Cases - 100 and Controls – 100 STUDY GROUP:
Patients admitted with a diagnosis of Acute Coronary Syndrome as diagnosed by symptoms, ST segment elevation or depression, T wave changes, in contiguous leads and who presented to the hospital within 24 hours of onset of symptoms and who were willing to give informed consent. The control population included patients presenting to the hospital with any other diagnosis apart from myocardial infarction, unstable angina or non ST elevation such as Dilated Cardiomyopathy, Hypertension with atypical chest pain.
INCLUSION CRITERIA:
FOR CASES:
· Patients admitted with a diagnosis of Acute ST Elevation Myocardial Infarction as diagnosed by symptoms, ST segment elevation in contiguous leads and echo correlation.
· Patients admitted with NON ST Elevation Myocardial Infraction, Unstable angina as diagnosed by symptoms, ST segment and T wave changes.
FOR CONTROLS:
· Patients with DCMP or CAD presenting with atypical chest pain.
· Patients with hypertension atypical chest pain.
· Patients with diabetes atypical chest pain.
· Patients with non cardiac chest pain (NACP).
EXCLUSION CRITERIA:
· Patients presenting with chest pain like
· Patients with dilated cardiomyopathy,
· Reinfraction in patients who gave already undergone CABG or angioplasty.
· Pericarditis.
· Patient who did not give consent for this study.
METHODS
Proper approval for this study was obtained from Institutional ethical committee of Chengalpattu Medical College. Written informed consent was obtained from each participant prior to inclusion in this study. Anthropometric measurements have been done by standard techniques. To ensure correct measurement of height subjects were asked to straighten their back and observer adjusted the head of the subject in Frankfort plane. Weight was measured by electronic weighing scale.
BMI was calculated in Kg/m2 from height and weight. BMI was used as a measure of obesity and subjects were classified as (in adult Asians), a person is considered
· Underweight - BMI <18.5 kg/m²;
· Normal - BMI 18.5 to 22.9 kg/m²;
· Overweight - BMI > 23 kg/m²;
· Obese - BMI > 25 kg/m².
Waist circumference was assessed with tape measure, at the trunk’s narrowest portion, between the ribs and the iliac crest at the moment of exhalation, with an unclothed waist,
· Abdominal obesity (waist circumference >90 in men and >80 cm in women).
Visceral Adiposity Index Females:
· VAI = (WC) / 36.58 + (1.89*BMI) * (TG/0.81) * (1.52/HDL)
Males:
· VAI = (WC) / 39.68 + (1.88*BMI) * (TG/1.03) * (1.31/HDL) where WC is expressed in cm, BMI in Kg/m2, TG in mg/dL,and HDL in mg/dL. Assuming VAI = 1 in healthy non obese subjects with normal adipose distribution and normal TG and HDL levels.
Biochemical Measures:
Fasting samples of subjects were obtained for triglycerides and HDL. To know the diabetic status fasting blood sugar and post prandial blood sugar were obtained. BP was measured using standard adult BP cuff in right upper arm in sitting position in mm of Hg.
Analysis:
Analysis has been done by comparing the groups using T – test and Chi-Square test depending upon the type of variable. Correlation is assessed by Pearson’s method.
RESULTS AND ANALYSIS
There are 200 participants in this study. The information was collected prospectively and the data were entered into MS-Excel sheet.
The collected data were analysed with IBM. SPSS statistics software 23.0 version. To describe about the data descriptive statistics frequency analysis, percentage analysis were used for categorical variables and the mean & S.D were used for continuous variables. To find the significant difference between the bivariate samples in Independent groups the unpaired sample t-test was used. To find the significance in categorical data Chi-Square test was used. In both the above statistical tools the probability value .05 is considered as significant level.
Table : GENDER DISTRIBUTION OF PARTICIPANTS
Sex Participants
Total Experimental Control
Female 39 45 84
Male 61 55 116
Total 100 100 200
This table shows that 200 people participated in our study, out of these 84 were females and 116 were males constituting about 42% and 58% respectively.
39 45
61 55
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Experimental Control
Gender distribution
Female Male
Table : Age Distribution of Participants
Age Participants
Total Experimental Control
Upto 40 yrs 11 7 18
41 - 60 yrs 50 56 106
61 - 80 yrs 31 34 65
Above 80 yrs 8 3 11
Total 100 100 200
This table shows that participants were mostly from the age group of 40 to 80 with majority from 41 to 60 years. Few people were also below 40 and above 80.
0 10 20 30 40 50 60
Agerange distribution
Experimental Control
Table: Diagnosis Diagnosis Participants
Total Experimental Control
CAD 0 25 25
DCMP 0 7 7
NACP 0 68 68
NSTEMI 19 0 19
STEMI 81 0 81
Total 100 100 200
Out of 100 experimental participants 19 were diagnosed to have NSTEMI and 81 were from STEMI. Matched were selected randomly from those admitted with chest pain due to other causes.
0%
20%
40%
60%
80%
100%
Experimental Control
Diagnosis
CAD DCMP NACP NSTEMI STEMI
Table: Prevalence of Diabetes in Participants
Diabetes Participants
Total Experimental Control
No 53 58 111
Yes 47 42 89
Total 100 100 200
Out of 200 participants 89 were with diabetes. Amongst them 47 were from experimental and 42 from control. Hence controls and cases are matched.
53 58
47 42
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Experimental Control
Diabetes
No Yes
